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Correlated Disturbances and U.S. Business Cycles


  • Ricardo Reis

    (Columbia University)

  • Vasco Curdia

    (FRB New York)


The dynamic stochastic general equilibrium (DSGE) models used to study business cycles typically assume that exogenous disturbances are independent with a simple structure for serial correlation. This paper relaxes this tight restriction, by allowing for disturbances that have a rich contemporaneous and dynamic correlation structure. Our first contribution is a new Bayesian econometric method that uses conjugate conditionals to make the estimation of DSGE models with correlated disturbances feasible and quick. Our second contribution is a re-examination of the sources of U.S. business cycles, using two canonical models, one real and the other monetary. We find that when we allow for correlated disturbances, the estimates of crucial parameters are more in line with other evidence, the impulse responses are closer to the results from vector autoregressions, and government spending and technology disturbances play a larger role in the business cycle, while changes in markups are unimportant

Suggested Citation

  • Ricardo Reis & Vasco Curdia, 2009. "Correlated Disturbances and U.S. Business Cycles," 2009 Meeting Papers 129, Society for Economic Dynamics.
  • Handle: RePEc:red:sed009:129

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    Cited by:

    1. Giovanni Angelini & Luca Fanelli, 2016. "Misspecification and Expectations Correction in New Keynesian DSGE Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(5), pages 623-649, October.
    2. Saroj Bhattarai & Jae Won Lee & Woong Yong Park, 2016. "Policy Regimes, Policy Shifts, and U.S. Business Cycles," The Review of Economics and Statistics, MIT Press, vol. 98(5), pages 968-983, December.
    3. Bask, Mikael & Madeira, João, 2011. "The Increased Importance of Asset Price Misalignments for Business Cycle Dynamics," Working Paper Series 2011:12, Uppsala University, Department of Economics.
    4. Mehkari, M. Saif, 2016. "Uncertainty shocks in a model with mean-variance frontiers and endogenous technology choices," Journal of Macroeconomics, Elsevier, vol. 49(C), pages 71-98.
    5. Monti, Francesca, 2015. "Can a data-rich environment help identify the sources of model misspecification?," Bank of England working papers 527, Bank of England.
    6. Fabio Milani, 2011. "Expectation Shocks and Learning as Drivers of the Business Cycle," Economic Journal, Royal Economic Society, vol. 121(552), pages 379-401, May.
    7. Negro, Marco Del & Schorfheide, Frank, 2013. "DSGE Model-Based Forecasting," Handbook of Economic Forecasting, Elsevier.
    8. Patrick Fève & Jean-Guillaume Sahuc, 2015. "On the size of the government spending multiplier in the euro area," Oxford Economic Papers, Oxford University Press, vol. 67(3), pages 531-552.
    9. Pablo A. Guerrón-Quintana & James M. Nason, 2013. "Bayesian estimation of DSGE models," Chapters,in: Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 21, pages 486-512 Edward Elgar Publishing.
    10. István Kónya, 2011. "Convergence and Distortions: the Czech Republic, Hungary and Poland between 1996–2009," MNB Working Papers 2011/6, Magyar Nemzeti Bank (Central Bank of Hungary).
    11. Meyer-Gohde, Alexander & Neuhoff, Daniel, 2015. "Solving and estimating linearized DSGE models with VARMA shock processes and filtered data," Economics Letters, Elsevier, vol. 133(C), pages 89-91.
    12. Nikolay Gospodinov & Damba Lkhagvasuren, 2014. "A Moment‐Matching Method For Approximating Vector Autoregressive Processes By Finite‐State Markov Chains," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 843-859, August.
    13. Christoffel, Kai & Kilponen, Juha & Jaccard, Ivan, 2011. "Government bond risk premia and the cyclicality of fiscal policy," Working Paper Series 1411, European Central Bank.
    14. Bachmann, Rüdiger & Bayer, Christian, 2013. "‘Wait-and-See’ business cycles?," Journal of Monetary Economics, Elsevier, vol. 60(6), pages 704-719.
    15. Kónya, István, 2011. "Növekedés és felzárkózás Magyarországon, 1995-2009
      [Growth and convergence in Hungary, 1995-2009]
      ," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(5), pages 393-411.
    16. Chollete, Loran & Ismailescu, Iuliana & Lu, Ching-Chih, 2014. "Dependence between Extreme Events in the Real and Financial Sectors," UiS Working Papers in Economics and Finance 2014/12, University of Stavanger.

    More about this item

    JEL classification:

    • E1 - Macroeconomics and Monetary Economics - - General Aggregative Models
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles


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